PTE-relaxed drops the singlepredicate constraint, and can be thought of as a 'bag-of-constituents' model.

in the task of Entailed Relation Recognition, a corpus and an information need are specified.


In our problem setting, however, we have no negative examples at the initial stage.

uncertainty sampling without EM (Takayama et al.


We can retrieve positive examples from Web archive with high precision (but low recall) by manually augmenting queries with hypernyms or semantically related words (e.g., "Loft AND shop" or "Loft AND stationary").

we select several example data sets from Japanese blog data crawled from web.


We applied 8 graph connectivity measures (weighted and unweighted versions of average degree, cluster coefficient, graph entropy and edge density) separately on each of the clusters (resulting from the application of the chinese whispers algorithm).

similarly, 19 negative examples that were judged as compositional were collected (Table 1).


The problem is indeed qualitatively different: we do not have to choose among the head words competing for a role (as in the papers above) but among selectional preferences competing for a head word.

distributional similarity has also been used to tackle syntactic ambiguity.


Together with a set of constants, it de-fines a Markov network with one node per ground atom and one feature per ground clause.

as telephone-based information access systems become more commonly available to the general public, the inability to deal with non-native speakers is becoming a serious limitation since, at least for some applications, (e.g.


Because we adopted discriminative models for argument identification, we can easily add new features.

features used in this paper are shown in Table 1.


This is because our method preferred NULL phrases over unreliable phrases appearing before the predicate sentence.

we evaluated the precision and recall rates, and F scores, all of which were computed by comparing system output and the correct answer of each argument.


In another aspect in dialogue systems, certain dialogue patterns indicate that ASR results in certain positions are reliable.

when the window width is N , the rates are calculated by using only the last N utterances, and the previous utterances are discarded.


These features attempt to approximately encode changes in the grammar rules between source and target sentences.

here, Examples has been dropped, while for editors which has Examples as a head is retained.


This clearly indicates that there is a strong negative correlation between likelihood of occurrence of a non-query-term and ROUGE-2 score.

most of the current automatic summarization systems rely on a sentence extractive paradigm, where key sentences in the original text are selected to form the summary based on the clues (or heuristics), or learning based approaches.


To help the grammar developer decide the priority of problems to fix, we also output the count of items observed with the given CP and RP.

this is generally at the cost of reduced coverage, due both to the difficulty of providing analyses for all phenomena, and the complexity of implementing these analyses.


We then compare our results to the DUC participating systems.

both of the abovementioned "combine-then-rank" and "rank-thencombine" approaches have a common drawback.


From combination point of view, the newly proposed model can be considered as a novel method going beyond the conventional post-decoding style combination methods.

the grammar formalism determines the intrinsic capacities and computational efficiency of the SMt systems.


Experimental results show that SSGbased model achieves significant improvements over the FSCFG-based model and LSTSSG-based model.

from expressiveness point of view, the former usually results in more ambiguities than the latter.


In the case where two intermediate nodes share the same intermediate rule anchored to the same forest nodes, they can be shared.

we found that on average, intermediate nodes introduced in the forest are used in 4.5 different rules, which accounts for the speed increase.


We would like to draw attention to Hidden Markov Tree Models (HMTM), which are to our knowledge still unexploited in the field of Computational Linguistics, in spite of highly successful Hidden Markov (Chain) Models.

although Hidden Markov Models belong to the most successful techniques in Computational Linguistics (CL), the HMTM modification remains to the best of our knowledge unknown in the field.


Like in HMM, it is the notion of hidden states that facilitates "summarizing" distributed information and finding the global optimum.

3 Tree Transfer as a Task for HMTM HMTM Assumptions from the MT Viewpoint.


So, one solution to this unbalanced class distribution is to split the 'nonemotion' (emo_ntrl) class into several subclasses.

each feature value is boolean in nature, with discrete value for intensity feature at the word level.


Thus, when performing a task such as Question Answering (QA), many new aspects have to be taken into consideration.

in Table 3, the OQA missed some of the answers due to erroneous sentence splitting, either separating text into two sentences where it was not the case or concatenating two consecutive sentences; thus missing out on one of two consecutively annotated answers.


Furthermore,questions 15,16,18,19 and 20 contain both factual as well as opinion aspects and the OQA system performed better than the TQA, although in some cases, answers were lost due to the artificial boosting of the queries containing NEs of the EAT (Expected Answer Type).

we take into account the first 1, 5, 10 and 50 answers.


Denote A i as the multilabel in Y i that corresponds to the nonleaf categories and Sib(z) denotes the sibling nodes of z, that is the set of nodes that have the same parent with z, except z itself.

there are different kinds of loss functions l(y,ȳ).


Λ(y) is the feature representation of y.

hierarchical categorization methods can be divided in two types: local and global approaches (Wang et al., 1999;Sun and Lim, 2001).


Inspired by the example in Figure 1, we emphasize it is also important to separate the ancestor node in the correct path from their sibling node.

we can classify a document x to label y : where F (•) is a map function.


We compare a baseline relying on word frequency measures with one combining word frequency with shallow linguistic features.

due to the sparseness of tagged instances, words that occur with a very high frequency in the corpus automatically receive a lower score than low-frequent words.


After clustering, we can get a specification tree resembles the one in subsection 2.1.

the proposed approach is based only on product specifications.


when the features in a sentence are correctly recognized, Words describing those features are likely to be identified by our methods.


We consider the meanings of words per the above example; we will recognize "có", a meaning word in our meaning word set, which reflects a possessive relationship between "Máy tính" and "dung l ng RAM l n nh t".

if c is a concept, its key phrases, its title name, its redirect name and its category are all added as entries in the ViDic.


If so, then how much does our classifier help compared to querying random translations?

in this fashion iDiOM searches for images that are relevant to the intended concept as opposed to using a possibly ambiguous query.


Table 2 shows the break-down of the distribution of sentence types.

the association language patterns can capture word relationships in sentences, thus yielding higher performance than the baseline system using single words alone.


Instead, they are usually composed of the words with long-distance dependencies, which cannot be easily captured by n-grams.

an association language pattern is defined herein as a combination of multiple associated words, denoted by the task of association pattern mining is to mine the language patterns of frequently associated words from the training sentences.


Our method (travel blog) was much better than the generic blog method, which indicates that travel blogs are a useful information source for the extraction of travel information.

we used the value of k=4, which was determined in a pilot study.


Among the 50 errors, 5 entries (10%) were too short (fewer than four sentences) to be identified by our method.

it is costly and time consuming to compile travel information for all tourist spots and to keep them up to date manually.


We present the game rules design, the preparation of the game documents and the evaluation of the players' score.

this fact has encouraged a formulation of an alternative way of data collection, "Games With a Purpose" methodology (GWAP), (van Ahn and Dabbish, 2008).


In TCE_DI, term delimiters are identified first.

in this work, a new feature on the relevance between different term candidates is integrated with other features to validate their domain specificity.


Even if the translations from the source and target languages are semantically transferred to the intermediate language, lexically it is rarely the case.

we consider each such source-target pair a translation candidate.


The similarity of the two expanded pivot language descriptions gives a better indication on the suitability of the translation candidate.

the recall value of a manually created Japanese-English dictionary is higher than any automatically generated dictionary's value ( Table  We evaluated 2000 randomly selected translation pairs, manually scoring them as correct (the translation conveys the same meaning, or the meanings are slightly different, but in a certain context the translation is possible: 79.15%), undecided (the translation pair's semantic value is similar, but a translation based on them would be faulty: 6.15%) or wrong (the translation pair's two entries convey a different meaning: 14.70%).


The speed of this step is well over 6K tokens/hour.

in addition, the PATB part 1, part 2 and part 3 data is automatically converted into CATiB representation.


The document-level pre-filtering reduces the overall processing time by about 40 % (from 4 to 2.5 days on a 100-CPU cluster).

the search is initialized by adding a single hypothesis for each target sentence t m ∈ Θ to the stack for j = 1: During the left-to-right search , state transitions of the following type occur: where the partial score is updated as: Here, f (1, j, i) is a partial feature vector computed for all the additional source and target positions processed in the last extension step.


For English sentence preprocessing, we use the Stanford parser with output of typed dependency relations.

this null alignment ratio is relatively high in comparison to the French-English alignment, in which about 9% of French sentences and 6% of English sentences are not aligned.


The definition of the compositional property does not allow re-ordering.

many phrase pairs in the phrase table have joint and both marginal frequencies all equal to 1.


On both the reordering classification and a Chinese-to-English translation task, we show improved performance over a baseline SMT system.

we used GIZA++ to produce alignments, enabling us to compare using a DPR model against a baseline lexicalized reordering model (Koehn et al., 2005) that uses MLE orientation prediction and a discriminative model (Zens and Ney, 2006) that utilizes an ME framework.


We will also apply DPR on a larger data set to test its performance as well as its time efficiency.

the training times for ME models are usually relatively high, especially when the output classes (i.e.


Belkin and Goldsmith (2002) applied spectral analysis to understand the struture of morpho-syntactic networks of English words.

this work is restricted to the study of English DSNs only 1 .


Semantic DSN: The construction of this network is inspired by (Lin, 1998).

usually, the co-occurrence patterns with respect to the function words are used to define the syntactic context, whereas that with respect to the content words define the semantic context.


A paraphrase generation tool usually starts with a sentence which may be very similar to some potential solution.

this method, based on large graph exploration by Monte-Carlo sampling, produces results comparable with state-of-the-art paraphrase generation tools based on SMt decoders.


The total cost can be represented as: where l f is the cost to code the index of the feature and l θ is the number of bits required to code the coefficient of the selected feature.

we used "foreground/background" clustering to find cohesive clusters for various word senses in the ONTONOTES data, considering both "semantic" and "syntactic" similarity between the word senses.


Similarly, other meanings of "dismiss" (as in "dismiss an idea") should not share features with "fire".

ℓ 0 penalty methods, like stepwise feature selection, give approximate solutions but produce models that are much sparser than the models given by ℓ 1 methods, which is quite crucial in WSD (Florian and Yarowsky, 2002).


Experimentation shows that the Bayesian decision framework is superior to the use of likelihoods for segmentation.

there are also a large number of chain continuations across the utterance boundary, which implies that a story boundary is less likely.


Pitch accents are characterized not only by their absolute pitch height, but also by contrast with neighboring syllables.

in addition, we aim to compare the impact of different sources of training data.


We experimented with four different language models.

we described the first stochastic morphological parser for Turkish and gave two applications.


The binary plus ⊕ and the times ⊗ operations of the counting semiring are defined as the sum of the weight vectors.

the turkish alphabet includes six special letters (ç,g, ı,ö, ş,ü) that do not exist in English.


One can use a stochastic morphological parser to do spelling checking and correction, and present spelling suggestions ranked with the parser output probabilities.

the n th value of the vector in the counting semiring just counts the appearances of the n th arc of mt in a path.


In the model used in this work, the most common completions are NN, NNS, and NNP.

see Figure 3 for a simple visual example of how this works.


For clarity, we define the local factors as: Note that we can ignore the subscript t at Ψ 2 t (y t−1 = j, y t = i) by defining an HMM-like model, that is, transition matrix Ψ 2 j,i is independent of t. As exact inference, we use the forward-backward procedure to calculate marginals .

time complexity is O(t |Y| 2 ) for exact inference (i.e., forward-backward and Viterbi algorithm) of linear-chain CRFs (Lafferty et al., 2001).


linear-chain), an exact inference can be obtained efficiently if the number of output labels is not large.

this scheme uses only supported features that are used at least once in the training examples.


Second row: time versus testing accuracy/F1.

,we can minimizeA t (z t ),∀t simultaneously, and then update w t ∀t together.


7) For each candidate particle calculate the fitness function.

table 1 shows that, in all datasets, accuracy improved up to 9% by optimizing the cost of each edit operation.


The acceptance probability then just depends on the graph distances.

see Figure 5 (this is just for the auth graph).


(2) Another limitation is that the sentence scores calculated from existing methods usually do not have very clear and rigorous probabilistic interpretations.

we have Here we use parameter U st for the probability of choosing base model s given topic t, p(S i = s|T i = t) = U st , where s U st = 1.


In the GIVE scenario (Byron et al., 2009), users try to solve a treasure hunt in a virtual 3D world that they have not seen before.

to the online experiment, 31% of participants were male and 65% were female (4% did not specify their gender).


Integrated NLG systems have a simpler architecture because they do not need to model interactions between modules.

in this case the realizer produced only 16 solutions, all of which maintained referential coherence.


In the second case, we used a grammar with the same trees, but annotated with discourse referents.

they still face the problem of computational complexity that was originally solved by the pipeline model.


Online product reviews are a crucial source of opinions about a product, coming from the people who have experienced it first-hand.

any other sentence in a review that does not fit the above definition of an opinion sentence is considered as a non-opinion sentence.


The set of dependency relations is specific to a given parser -we use the Stanford parser 1 for computing dependency relations.

in such a situation, one critical issue might be the sparseness of the very specific linguistic features, which may cause the classifier learned from such features to not generalize.


A study of the inter-annotator agreement between two human annotators has been performed on a set of 100 questions.

(Table 1), assuming the system only tagged operas as AP, lenient accuracy will be 1, exact accuracy will be 0, precision for the AskingPoint class will be 1 and its recall will be 0.5.


We consider that AP takes precedence over the EAT.

the distribution of AP classes in the annotated data is shown in the table 2.


For each abstract sentence, we assign a score to every document sentence as the sum of its filtered BE scores divided by the number of BEs in the sentence.

the use of kernel is based on the accuracy we achieved during training.


For supervised learning methods, huge amount of annotated or labeled data sets are obviously required as a precondition.

every abstract sentence contributes to the BE score of each document sentence and we select the top N sentences based on average BE scores to have the label +1 and the rest to have the label −1.


If a clause does not have any POS tag that can serve as a main verb (VB, VBD, VBP, VBZ), it is marked as missing a main verb.

our approach uses a regular grammar and alignment information to detect missing verbs and draws from examples in documents determined to be relevant to the query to insert a new verb translation.


Under this assumption, the translation of "被捕" in the above example should be placed in the position between "Saddam" and ".".

in practice, more than one VTG may be found in a clause.


Simard et al in 2007 even developed a statistical phrase based MT system in a postediting task, which takes the output of a rulebased MT system and produces post-edited target-language text.

readers could better determine if the modified sentence better captured the meaning of the source sentence.


First, we rewrite the summation in (3) as a difference of fractional harmonic numbers: 2 Using the recurrence for harmonic numbers: We then use the asymptotic expansion, H F ≈ log F + γ + 1 2F , omiting trailing terms which are O(F −2 ) and smaller powers of F : 3 Omitting the trailing term leads to the approximation in Antoniak (1974).

due to a misinterpretation of Antoniak (1974), GGJ06 use an approximation that leaves out all the P 1 (w) terms from (4).


There has been an increase in available N -gram data and a large amount of web-scaled N -gram data has been successfully deployed in statistical machine translation.

figure 2(a) shows an example trie structure.


The succeeding (n+1)-grams are stored in a contiguous region and sorted by the word id of w n+1 .

lossless representation of N -gram is a key issue even for lossy approaches.


We find that splitting words into their stem and suffix components using a morphological analyzer and disambiguator results in significant perplexity reductions of up to 27%.

the logprobability spent on the zero suffixes in the split+0 dataset has to be spent on trying to decide whether to include a stem or suffix following a stem in the split dataset.


We note that, in the case of the former experiments, the size of the unlabeled examples is increasing in the direction 98a to 91a.

if we use unlabeled data within the epoch of the test set, we hardly see a degradation trend as the time gap between the epochs of seeds and test set is increased.


This can be achieved using MapReduce, a distributed computing framework (Dean, 2004) Elsayed et al.

here we describe some results from a system we built to perform this task on Arabic documents.


For example, in a "Conflict-Attack" event, "Attacker" and "Target" are more important than "Person" to indicate the event time.

various situations are evolving, updated, repeated and corrected in different event mentions.


For example, in the following we can propagate "Saturday" from a "Justice-Convict" event to a "Justice-Sentence" event because they both involve arguments "A state security court/state" and "newspaper/Monitor": [Sentence including EM i ] A state security court suspended a newspaper critical of the government Saturday after convicting it of publishing religiously inflammatory material.

it will be valuable to design inference methods for more fine-grained events.


Topic models have been used to study popularity of communities (Griffiths and Steyvers, 2004), the history of ideas (Hall et al., 2008), and scholarly impact of papers (Gerrish and Blei, 2010).

we ran 50 iterations for both categories, which was chosen as a reasonable trade-off between pattern precision and recall based on some earlier pilot experiments.


We introduce a dataset of abstracts labeled with the three categories.

topic models do not extract detailed information from text as we do.


all the experiments are evaluated using commonly-used Precision (P), Recall (R) and harmonic F1-score (F1).


Finally, since the dependency type between "PROT1" and "Association" is prep_between, the preposition word "between" and its constituent ancestors are added into the SCP as rendered by the dotted lines.

this paper proposes a principled way to automatically generate constituent structure representation for tree kernel-based protein-protein interaction (PPI) extraction.


Since determining protein interaction partners is crucial to understand both the functional role of individual proteins and the organization of the entire biological process, there is a significant interest in protein-protein interaction (PPI) extraction.

4) Merge any two consecutive NP/VP nodes along the paths into a single one.


This indicates the importance of the shortest dependency path over other paths in the dependency path tree or the dependency graph.

we can reshape the constituent parse tree by making use of the shortest dependency path between two proteins.


2007), graph kernels (Airola et al., 2008) and subsequence kernels (Kim et al., 2010), show some promising results for PPI extraction.

while tree kernels based on constituent parse trees achieve great success in semantic relation extraction (Zhang et al., 2006;Zhou et al., 2007a;Qian et al., 2008) and semantic role labeling (Moschitti, 2004;Zhang et al., 2008) from the newswire narratives, they haven't been fully explored for PPI extraction in the biomedical domain.


In our experiments, we first take dev.a as our development set for minimum-error rate tuning (Och, 2003) and then report the final translation accuracies on dev.b.

most of current syntax-based SMT systems use IBM models (Brown et al., 1993) and hidden Markov model (HMM) (Vogel et al., 1996) to generate word alignments.


Our results show that source-reordering is beneficial for the language pairs with high mutual word order disparity.

in this paper we take the idea of learning source permutation one step further along a few dimensions.


Subsequently, the siblings under S in the resulting tree are permuted, "must" is reordered across the whole clause and placed to the first position (see Figure 3c).

the model in (Li et al., 2007) is explicitly aimed at long-distance reorderings (English-Chinese), prunes the alignment matrix gradually to fit the source syntactic parse and employs Maximum-Entropy modeling to choose the optimal local ITG-like permutation step of sister subtrees but interleaves that step with a translation step.


In order to illustrate the performance of the different reordering models, we consider two training sentences taken from the IWSLT 2010 DIALOG task.

for the previous orientation weight, the probability P c (p, o) of the phrase pair p having a given orientation o, considering the set P rev(o), with all phrase pairs that are linked to p that would lead to a orientation o, is given by: In this equation, α(p ) is the number of paths to the p node from the first phrase, β(p) is the number of paths from p to the last phrase and β(bs) results in the number of possible paths.


In this case, the phrase pair "不需要 (bu xu yao)"→"need not" and the phrase pair "它 (ta)"→"it" are linked by an edge even though they are not adjacent.

if we translate "疼 (teng)" by itself, we would have to translate "很 (hen)" without "疼 (teng)".


If the learner is uncertain about an instance, that shows that the learning model is not able to deal with the instance properly.

the original corpora was randomly partitioned into 5 parts, out of which, a single part was retained for testing the model, and the remaining 4 parts were used for the training and applying our instance selection strategies.


The test data was only used for the final accuracy report.

based on the work of Andrew and Gao 2007, we know that OWL-QN method guarantees the convergence.


All pairs knew each other previously and were of the same gender and approximately the same age.

the task becomes more complicated than typical coreference resolution for written texts because a referent is considered as either anaphoric (i.e.


The entries of each chromosome are randomly initialized to either 0 or 1.

in general, in the t th iteration, a combined population R t = P t + Q t is formed.


In anaphora resolution, 1 as in other HLT tasks, optimization to a metric is essential to achieve good performance (Hoste, 2005;Uryupina, 2010).

this is obviously less than the evaluation figure obtained by optimizing the first metric.


Event coreference resolution is an important task in natural language processing (NLP) research.

the arguments information is extracted automatically from the premodifiers and propositional phrase attachments.


Without a successful event coreference resolution such separated pieces of information cannot be assembled properly.

ideally, the selected instances should represent the coreferent status between any two mentions.


Last section will wrap up with a conclusion and future research directions.

we propose a revised training instance selection strategy which reflects the true sample space of the original coreferent/non-coreferent status between mentions.


Any verb phrase alignment pair with score lower than a threshold score of 0.5 is ignored.

there are still some errors in respect to the morphology of the verb phrases, which the Language Model is unable to tackle.


We do not have a list of transliteration pairs for the training of our Hindi to Urdu transliteration system.

the authors wish to thank the anonymous reviewers for their comments.


It is complicated to define different semantic types, and is tedious to train a large number of models used for different semantic information.

this semantic information for translation is various and difficult to classify.


The similar action is applied to SC (the context of k sc ).

semantic information is various for NE translation.


Because the proposed three features cannot be used separately, we do not compare their individual effectiveness.

they should be weighted differently according to their contributions.


These figures reveal that both distributions of deletion and insertion are comparable.

our approach differs from them in that we employ the wisdom of crowds of native speakers, not necessarily language teachers, to compile a large-scale learners' corpus.


Our work differs from them in that we (1) do not restrict ourselves to a specific error type such as mass noun; and (2) exploit a large-scale real world data set.

both models corrected the examples below: Original: the model trained on 0.3M sentences corrected the following example: Original: (the learner made an error in conjugation form.)


For comparison purposes we include in this table results from using only first level features (FLF), only modality specific meta features (MSMF), and the combination of both (MSMF+FLF).

we also include percentages of capitalized words, use of quotations, and use of signature, that we believe allow writers more freedom to express their unique writing style.


That means that we end up with different arrangements of the training instances into clusters, one arrangement per modality.

in the typical scenario, we may have an entire document (several pages long), or even an entire book, while in the case of online data from social media we will have very short texts that are a couple of sentences long.


The meta features in our work are derived from clustering of the feature vectors.

the results in table 8 show that the best accuracy in the CHE collection is achieved by our method in all four data sets.


The last section summarizes our findings and outlines our research goals for the immediate future.

to the best of our knowledge, this is the first work exploiting characterbased language models for AA, although, Raghavan et al.


For larger k values only the data set with 5 authors reached better results.

in our framework instead of having a single feature vector for x, we generate m smaller vectors that contain complementary types of features, or views, describing the instances.


The features include TF•IDF, "First occurrence", and "Is in title or not".

a simple hill-climbing search can be used to optimize F1-Score.


This paper is concerned with the TREC REF track.

many semi-supervised methods have been proposed to recognize fine-grained types of entities.


Fleischman (2002) employed a supervised learning method that considered the local context surrounding the entity as well as global semantic information.

approaches proposed in expert search can be used for entity finding.


It has been reported that SRL can benefit from phrase-structure and dependency-based syntactic parsing (Hacioglu, 2004 Pradhan et al., 2005).

much research efforts have been devoted to statistical machine learning methodologies for SRL (Bjkelund et al., 2009;Gildea and Jurafsky, 2002;Shi et al., 2009;Johansson and Nugues, 2008a;Lang and Lapata, 2010;Pradhan et al., 2008;Fürstenau and Lapata, 2009;Titov and Klementiev, 2011, among others).


We also applied the solution of combining informativity and representativeness (4.3) to other informativity-based strategies.

ours is the first work to use such a combined strategy for SRL.


Using an HMM that has been initialized with grammar-based transition probabilities, combined with a two-stage integer programming strategy, this approach can achieve single-best accuracy of 64.3% on ambiguous supertags.

the identifier and classifier models are then trained from these features, instead of from features obtained from gold-standard syntactic derivations.


we regarded the predicate of " (do) + particle + noun" such as " " in Example (4) as " (do)."


When training with the full feature set, the suffix1=国 predicate is active for all of those country names and has a large feature weight associated with the preferred latent tag.

the no+simple row in table 2 represents the baseline, for which the grammars are trained without rare word smoothing described in Subsection 2.1 and OOV words are handled by the simple method described in Subsection 2.2.


Ng (2010) presents a comprehensive review of recent approaches to within-document coreference resolution.

candidate ranking then considers each candidate in greater detail, producing a ranked list.


Our approach depends on having a quantity of labelled data on which to train a classifier.

we apply English NER to find person names in text (Ratinov and Roth, 2009), our English entity linking system to identify candidate entity IDs, and English annotators on Amazon's Mechanical Turk to select the correct  kbid for each name.


For , we use the probabilities of alignment pairs which are computed by GIZA++.

they usually need manuallyannotated high performance textual corpora.


We propose an improved latent topic model by introducing the category information of questions.

mAP rewards methods that return relevant questions early and also rewards correct ranking of the results.


We frame the task of selecting event-relevant sentences as a binary classification problem over all sentences in the corpus.

our semi-supervised learning approach balances the pros and cons of both supervised and unsupervised approaches.


Table 6 shows the results of the extrinsic evaluation.


If we assume an unmarked character as substitution error of one voiced consonant to one voiceless consonant, the task of detecting an unmarked character can be considered as a kind of error correction.

most Japanese people regard the phrase to the Meiji and Taisho Era; we also use the phrase to intend the narrower sense.


In Table 1, we present the statistics of the voiced consonants in "Kokumin-no-tomo" corpus which we will use for our evaluation.

the frequency of each Kanji character varies.


Procedure 1 is a pseudocode representation of the OT method.

given a phoneme a x , we setP (a x ) to be the maximum likelihood estimate: where n ax is the number of times a x occurs in D train , n * = p∈Π n p , and α 0 is the unigram smoothing constant.


Following the previous work (Zhao et al., 2006;Zhao et al., 2010), we employ the linear chain CRFs (Lafferty et al., 2001) as our learning model.

the best score in each column is in boldface.


We also tried to use different lexicons, as well as representing the feature with only one POS tag with the highest frequency.

z&C 10 refers to zhang and Clark (2010).


The phrase-level sentiment annotation focuses sentiment annotation on phrases not words with concerning that atomic units of expression is not individual words but rather appraisal groups (Whitelaw et al., 2005).

among the five studied algorithms, the T-S algorithm is best to be employed by the proposed LFS framework.


We randomly divide the labeled data set into five folds so that each fold at least contains one example instance labeled by each attribute node.

to test which is the best to be employed by the proposed LFS framework, we further comparatively study five classic feature selection algorithms respectively based on document frequency (DF) (Manning et al., 2008), mutual information (MI) (Manning et al., 2008;Church and Hanks, 1990), χ 2statistic (CHI) (Manning et al., 2008), information gain (IG) (Mitchell, 1997), and term strength (t-S) (Wilbur and Sirotkin, 1992).


In the HL-SOT approach, each target text is encoded by a vector in a globally unified d-dimensional index term space and is respectively labeled by different nodes 2 of SOT in a hierarchical manner.

the measurement for selecting terms as features should be calculated among nodes on the same hierarchy.


The approach aims at classifying both sentiment expressions as well as their targets using a rich set of linguistic features.

we convert the tree structure to a linear sequence of relations between neighboring segments.


By following the compositionality-based polarity decision rules, the polarity of a noun or a noun phrase that has no inherent polarity is determined by its modifier's polarity.

detecting the sentiment of a given grammatical unit such as phrase, clause or sentence has received much attention in opinion mining and sentiment analysis.


By considering these two sentences together, we can see that a 'negative' sentiment is conveyed.

in some contexts, the polarity of the adjectival modifier may not always be correctly determined by such rules, especially when the adjectival modifier characterizes the noun so that its denotation becomes a particular concept or an object in customer reviews.


To generate the n th word in document d we draw the topic id z n d from the document-specific topic distribution θ d , and then draw the word from the word distribution corresponding to the chosen topic φ zn d .

the training set consists of sentences annotated with the relations and their directionality.


4) can improve the performance; and (2) whether the performance can be improved if using unlabeled data (see D k in Eq.

1) with all the features, and then remove one group of features each time.


Not as expected, removing the length features does not lead to as a remarkable drop as removing other features (p-value=0.12).

when a user issues a query, recommending tweets of good quality has become extremely important to satisfy the user's information need: how can we retrieve trustworthy and informative posts to users?


The performance was boosted with such a factor.

we adopt a sentiment lexicon (SentiWordNet) and collect the top 200 frequent emoticons from our tweet corpus to identify positive and negative sentiment words.


To label the target language documents automatically we propose a method called crosslanguage guided clustering (CLGC).

second, a key assumption made in both these approaches is that the class labels across languages are completely shared.


This cluster mapped to a concept in Hindi which had words such as " Ê ", " " and " " where the first two words are translations for the words "communication" and "retail" respectively.

an edge is added between every pair of vertices (C S i , C T j ) where the weight of the edge is given by Sim X (C S i , C T j ).


For the relation descriptor extraction problem, however, we expect that there is either a single relation descriptor sequence or no such sequence.

let t i denote the POS tag of w i and p i denote the phrase boundary tag of w i .


A relation descriptor may also contain multiple relations.

the space of label sequences should be reduced to only those that satisfy the above constraint.


Based on our preliminary experiments, we have found that using a smaller set of general POS tags instead of the Penn Treebank POS tag set could slightly improve the overall performance.

while in linear-chain CRFs the correct label sequence competes with all possible label sequences for probability mass, for our task the correct label sequence should compete with only other valid label sequences.


Table 1 illustrates the actual aaa, ac compressors, acheron, acrocyanosis, adelaide cbd, african population, agua caliente casino, al hirschfeld, alessandro nesta, american fascism, american society for horticultural science, ancient babylonia, angioplasty, annapolis harbor, antarctic region, arlene martel, arrabiata sauce, artificial intelligence, bangla music, baquba, bb gun, berkshire hathaway, bicalutamide, blue jay, boulder colorado, brittle star, capsicum, carbonate, carotid arteries, chester arthur, christian songs, cloxacillin, cobol, communicable diseases, contemporary art, cortex, ct scan, digital fortress, eartha kitt, eating disorders, file sharing, final fantasy vii, forensics, habbo hotel, halogens, halophytes, ho chi minh trail, icici prudential, jane fonda, juan carlos, karlsruhe, kidney stones, lipoma, loss of appetite, lucky ali, majorca, martin frobisher, mexico city, pancho villa, phosphorus, playing cards, prednisone, right to vote, robotics, rouen, scientific revolution, self-esteem, spandex, strattera, u.s., vida guerra, visual basic, web hosting, windsurfing, wlan Table 2: Set of 75 target instances, used in the evaluation of instance attribute extraction ranked lists available in the repository for various phrases.

each query is accompanied by its frequency of occurrence in the query logs.


If the input set of queries increased, additional candidate attributes would be extracted.

"kelley blue book value of 2008 dodge charger" and "kelley blue book value of 2008 honda civic" can be grouped into a shared query template "kelley blue book of 2008 ⋆", whose slot ⋆ is filled by the names of car models.


Besides above reasons, 280 out of 1,916 queries did not exist in clickthrough logs, resulting in our system not being able to extract the correct query.

murayama and Okumura (2008) formulated the process of generating Japanese abbreviations by noisy channel model but they did not handle abbreviation expansion.


These queries are possibly synonyms with the input query and thus possible to correct without semantic transformation.

we extracted 50 candidates from clickthrough logs and then reranked using three methods: 1.


Although these are high-quality corpora, they have some limitations: (1) they tend to be domain specific (e.g., government related texts); (2) they are available in only a few languages; and (3) sometimes they are not free or there is some restriction for using them.

to measure the quality of the BS Detector, we manually labeled 200 Web sites (100 positive and 100 negative) from the dmoz directory in topics related to Spanish speaking countries.


This part is an engineering task where the mapping should be best fitted to the corpus.

this model was motivated by Collins (1999)'s head-outward dependency model and Hockenmaier (2003)'s generative model for parsing CCG.


If both feature vectors contain only flat features, the subtraction is straightforward, since each flat feature is real-valued.

to reduce a ranking problem to an equivalent classification problem, we need to convert the training set for the joint CR model to an equivalent training set that can be used to train a classifier.


's (2001) method for creating training instances.

to do so, SVM light needs to position the hyperplane so that an instance with a higher rank in t is assigned a more positive value by the hyperplane than one with a lower rank in t .


Given an instance i(NULL, NP k ), we extract the substructure from the parse tree containing NP k as follows.

b 3 and CEAF F-measure scores rise by 1.0% and 1.9%, respectively.


", which show the error reduction of a system relative to the Baseline joint CR model.

this baseline model does not employ any tree-based or path-based features.


A potential issue is the proposed model might be sus-ceptible to the sparseness issue.

another line of research treats sentence compression as machine translation, in which tree-based translation models have been developed (Galley and McKeown, 2007;Cohn and Lapata, 2008;Zhu et al., 2010).


In Section 4 we introduce the decoding algorithm.

sLFs is an instance of the broader spectrum of text-to-text generation problems, which includes summarization, sentence compression, paraphrasing, and sentence fusion.


We explore three approaches to incorporate the query information in theme-based summarization, including query-driven cluster ranking, query-embedding similarity measure and semi-supervised clustering.

sentence clustering requires the affinity matrix that is built upon the cosine similarity between the two sentences.


When the given documents are all supposed to be about the same topic, they are very likely to repeat some important information in different documents or in different places of the same document.

the former document set contains 10 documents about 'Labor Dispute in National Basketball Association', while the latter one contains 25 documents about 'Art and music in public schools' and the corresponding query is to 'Describe the state of teaching art and music in public schools around the word, indicate problems, progress and failures'.


we used the texts as dialogue data in the experiments.


is percent correct calculated by (T-S-D)/T, and W.A.

the result indicates that the AS method (without RFU) can eliminate uninformative frequent utterances when generating an indicative summary or informative summary with a low compression rate.


For example, the generative process of lecture speech, with regards to a hierarchical structure (here, bullet trees), is char-acterized in general by a speaker's producing detailed content for each bullet when discussing it, during which sub-bullets, if any, are talked about recursively.

this type of approaches, however, are unlikely to recover semantic structures more detailed than slide boundaries.


As far as time complexity is concerned, the graph-partitioning models discussed above are quadratic with regards to N , i.e., O(M N 2 ), where M ≪ N ; M and N denoting the number of bullets and utterances, respectively, with the loop kernel computing and filling D[i, j, k] in equation 3, which is a M × N × N matrix.

the cohesion conveyed by the repetition of the words that appear in transcripts but not in slides could be additionally helpful; this is very likely to happen considering the significant imbalance of text lengths between bullets and transcripts, from which the alignment models by themselves may suffer.


These differences are statistically significant (p < 0.05).

our main goal is to efficiently identify questions covering a wide range of topics while matching a certain style, often represented by colloquial textual fragments and therefore consisting of frequent words.


We only need to use a small amount of annotated development data, 500 articles in our study to guide the instance selection to achieve similar performance as with unannotated test set being development data.

wikipedia requires contributors to assign categories to each article, which are defined as "major topics that are likely to be useful to someone reading the article".


However, from our observation some categories in Wikipedia may not be suitable to model the topics of a document.

in the instance selection process, we use the KB entries with more than 15 linked documents in the auto-generated data as our initial Training Set (1,800 instances) to train a classifier, and then use this classifier to select instance from the auto-generated dataset.


Another fact is that the selection process is similar to active learning, which needs to manually annotate the selected instances in each batch.


For the computational cost, we note that document annotation is performed offline, while queries are typically short and thus query annotation and expansion could be done quickly.

each term is a Ne feature, a WW feature, or a keyword.


Next, other nodes (web pages) of their network are activated.

mihalcea and moldovan (2000) use senses in both queries and documents, and all forms of every hypernym of a sense in a document.


Besides, there is latent information of the interrogative words Who, What, Which, When, Where, or How in a query.

a statistical significance test is required (Hull, 1993).


In the future, we will apply CLGVSM in more languages pairs and extend it in more than two languages.

experimental results on benchmarking data set show that (1) the proposed CLGVSM is very effective for cross-document clustering, outperforming the two strong baselines vector space model (VSM) and latent semantic analysis (LSA) significantly; and (2) the new feature selection method can further improve CLGVSM.


In that case, the associate matrix becomes sparse and computational time can be saved.

dictionary and corpus are two popular ways to get cross-language information.


Due to the quantity of spam postings is small, after calculating the similarity, the cluster which has the least postings may be filtered as the spam.

let i A represent the set of articles that are managed in a system-generated cluster , is the set of articles managed in a human-generated cluster .


Our approach is similar to HAC in nature.

it is not known whether the clustering algorithms are effective in microblog TD.


Once a thread text is assigned a topic label, microblog texts in this thread are all assign the label.

a clean and topic-related thread is obtained from thread .


Plural nouns are a property of general sentences.

some examples of sentences with different agreement levels are shown in Table 5.


Table 9: Mean value of confidence score on correct predictions Here, after combining the examples, the classifier learns the lexical features indicative of both types of articles.

to efforts in automatic discourse processing (Marcu and Echihabi, 2001;Sporleder and Lascarides, 2008), in our work we are not interested in identifying adjacent sentences between which this relation holds.


According to their significance to sentiment classification, we categorize the POS tags into four groups, as shown in Table 1.

the distribution of O changes the least.


Using different supervised learning classifiers on various sizes of training datasets, we improved and demonstrated the efficiency of our feature set by adding syntactic patterns extracted from POS tags.

small but comprehensive feature set: we only use a small set of features to describe a cita-tion sentence.


We argue that one major reason behind is a missing common understanding of genres and that we need to focus on the single aspects of genres in order to overcome this situation.

nevertheless, Figure 3b also conveys that we achieved 13.1% less accuracy in the smartphone domain than in the music domain (68.8% vs. 81.9%).


Though precision significantly dropped for commercial, given a class imbalance of 1:9 (commercial:rest), 61.9% is still over five times better than the expected precision of guessing.

additionally, Boese and Howe (2005) recognized that, in the web, genres may evolve over time to other genres.


For the writing style features, we determined the 48 most common words, the 55 most common part-of-speech trigrams, and the 35 most common character trigrams on the training set of each collection.

lFA is about the question why a text was written and, thus, refers to the authorial intention behind a text.


Similar problems refer to the analysis of speaker intentions in conversation (Kadoya et al., 2005) and to the area of textual entailment (Michael, 2009), which is about the information implied by text.

many different genre classification schemes exist, which makes most approaches to genre identification badly comparable as a recent study showed (Sharoff et al., 2010).


However, these three parts may belong to different domains, leading to distribution variations, which indicates that the features and weights learned from the data may be biasestimated.

the feature weight is further re-estimated based on both the development dataset and the test dataset with pseudo references.


Although the development of decoding algorithms is a key topic in SMT research, if we are to construct better SMT systems it is also important to find a way to determine the weights of different model components.

the outer loop first runs the decoder over the source sentences in the tuning dataset with the current weights to generate N -best lists of translations.


Finally, if we cannot find a new global best position around the current global best position in a certain number of iterations T c then the current global best position can be considered a local maximum.

hereafter, we assume a maximization problem.


We first observe that all models predicting a target vocabulary get better BLEU than the baseline.

we also observed that MLP-64 systems are significantly better than all the other three systems.


This sparsity helps the model to scale well to larger vocabulary, but it cannot model relations in translation beyond word cooccurrences.

we first tried with a fixed learning rates, but the results were disappointing.


14 Recall that the true weight is e a i 7 Conclusions and Future Work In this paper, we presented a novel method of cross-adaptation based system combination which obtains statistically significant BLEU gains over best single system.

using the dissimilarity cross-adaptation as a second additional system helps slightly more, bringing the total gain to 0.45 BLEu (CN w/ CA+DCA).


In this case, we instead measured the TER of the cross-adaptation output against the external input system hypotheses.

the GALE-only training data consists of 46 million words of LDC-released data plus 30 million words released by Sakhr Software.


These features do cause some amount of overfitting on the tuning set, but we have not found this to be harmful to the test sets.

although our new method does produce a significant gain over the best single system, it does not perform as well as our confusion network decoding on any condition that we tried.


The values {c The differentiation of this function with respect to ⃗ w can be performed in a fairly small number of steps using basic calculus, the details of which are provided in (Devlin, 2009).

10 we used the input hypotheses as reference translation and optimized in the opposite direction that we normally would.


U# in the table shows the number of used unlabeled instances.

by applying the classifier form and training method presented in , we defined P(s|x) as We also provided the objective function, J for the parameter estimation of P(s k |x;W, Θ, β ) by using labeled and unlabeled datasets, Here, β (> 0) is a combination weight.


Such sentences are informative, but in most case of paper dictionaries such as Iwanami, the examples are fragmentary to save spaces (in Iwanami, an average of 4 words).

features for each target word w, we used the surface form, the base form, the POS tag, and the coarse POS categories, such as nouns, verbs, and adjectives of w. Then we also used bag-of-words in the same sentence.


We used all extracted labeled instances when the number was less than #.

we attempt to extract longer, more natural and high quality labeled data from the raw corpus, under strict conditions using fragmentary examples.


Another type of method designed to make up for a lack of training data, is automatic labeled data expansion (Mihalcea and Moldovan, 1999;Agirre and Martinez, 2000) .

when using labeled data for semi-supervised learning, minimum expansion provides the best performance, and protects the system against sense bias.


The main aim of this step is to obtain reliable labeled data even for unseen and infrequent senses.

no method based on given training data alone (Trn) can guess these senses correctly.


Polysemy is the tendency for ConceptNet concepts to have multiple senses.

in addition, ConceptNet defines nearly thirty kinds of semantic relations, such as CapableOf (agent's ability), SubeventOf (event hierarchy), MotivationOf (affect), Desir-eOf (want to), and so on, most of which are not included in WordNet.


This paper proposes a novel idea of combining WordNet and ConceptNet for WSD.

for ambiguous concepts with multiple senses, there are multiple nodes in the network.


Of course, in real applications, it is impossible to know which kind of noises exist and where they are in advance.

although hypernymy, hyponymy, and meronymy/holonymy are transitive, and can generate the indirect WordNet resources, the number of meronymy/holonymy is far below than those of the other two in the WordNet.


There are two kinds of noises: those from WSP of the correct sense, and those from WSP of the wrong sense(s).

obviously, this kind of noises have high NGD scores and would increase the score of the WSP of the correct sense, thus correspondingly decrease the probability of selecting the correct sense as the appreciated sense of ambiguous concept.


Ideally, the NGD score of any term in the WSP of the correct sense is lower than that in the WSP of incorrect sense, thus we can simply use the arithmetic mean of the scores to evaluate the relatedness of WSP and the assertion.

for example, the concept airplane has only one sense: "a fix-wing aircraft", which is also the first sense of the concept plane.


Therefore, if extending WordNet with the large amounts of semantic relations contained in ConceptNet, it is desirable to improve the performances of WordNet-based WSD methods.


Concept airplane has an atLocation relation with the concept airplane hangar, whereas plane has not such relation with airplane hangar.

we do not list the accuracies of combining other indirect resources with the direct ones).


Li and Li (2004) proposed an approach based on bilingual bootstrapping which does not need parallel corpora and relies only on in-domain corpora from two languages.

amongst all the POS categories, the performance of our algorithm is lowest for verbs.


These two steps are described in more details in the following sections.

(Afzal, 2009) observes a different hierarchy, DT > ME > NB, but on a different corpus and a different language, which makes the comparison difficult.


As the PageRank strategy only relies on the graph structure without considering the weight of the edges, its highest ranked entities are those that are highly connected regardless of the weight of the edges.

two entities are associated with different earthquake events.


As every slot is associated with an entity type, we need to select (when it is possible) one entity value for each slot.

we need to segment texts according to the events they refer to.


QuestionBank does not provide function tags, and therefore in training and evaluation of the parsers, abstracted dependencies were extracted from the corpus.

the problem is more serious for imperatives.


Table 6 gives the recall errors on labeled dependencies, which were observed more than ten times for 100 analysis sentences in each domain.

if these were embedded in another imperative or question, we only extracted the outermost one.


(2002) proposed a probabilistic model for zero pronoun detection and resolution that used hand-crafted case frames.

for instance, there are thousands of named entities (NEs) that cannot intrinsically be covered.


For example, although the dative argument of the predicate 'yaku (bake/burn)' is generally filled by a disk, such as a CD or DVD, it is often filled by a person, such as 'musuko (son),' if the accusative argument is filled by 'te (hands)' as in the example (i).

although most previous work has focused on zero anaphora in newspaper articles, 1 'Yaku' is the original form of 'yaiteiru.'


In addition, we generalized case slot examples based on automatically acquired multi-word noun clusters.

since we basically focused on the zero anaphora in Web text that only adhered slightly to formal grammar, we did not give priority to exploring effective syntactic patterns.


We were not able to obtain the sentence sets used in  and Cholakov et al.

the naive one assigns the most frequent expanded type in the lexicon, count-noun-le f, to each unknown word.


The methods are compared based on the effect their application has on the parsing coverage and accuracy of the GG grammar of German (Crysmann, 2003).

the average type precision and type recall for the 400 test words are given in table 5.


(2008) can define more features in the created lexical entry due to the detailed tagset used.

the focus is primarily on improving the parsing coverage and accuracy of the grammar for a particular input.


First, we compare the proposed method to both a traditional MST parser (McDonald et al., 2005), and a deterministic parser (Nivre and Scholz, 2004).

while statistical parsers achieve higher and higher accuracies on in-domain text, the creation of data to train these parsers is labor-intensive, which becomes a bottleneck for smaller languages.


Histograms for the 32K page size index -omitted here for lack of space -look similar to Figure 4 and Figure 5, but the compressed index shows a very marginal increase in the number of faster forests and a clear increase in the time for the slower forests.

our experiments test the performance of search with page size and compression of keys.


We feel that the most likely reason in this case is the first one.

the average forest search time with the baseline setup is compared with the best breadth-first index based search method in table 2.


Once a source language sentence is parsed into a packed forest and pruned, the next step is to find trees in the forest that have matching rules in the translation rule dictionary.

to the best of our knowledge, no index-based search structures for incrementally finding trees have been proposed to date.


The differences between languages can be seen in Figure  1, which shows an example of English-Japanese.

we incorporate dependency relations of words into the alignment model and define the reorderings on the word dependency trees.


EXPAND-1 does not have any restrictions on its operation.

combine the subtrees in each language so as to create parallel sentences.


This is the same for the TOGGLE operation.

we introduce a pseudo parent to capture the relations.


Given a paraphrased sentence T, which consists of J words, the novelty function Novel(TM,T,n,j) judges whether the occurrence of t j generates a new n-gram to the translation model (TM) according to the prior n-1 words of t j .

the toolkit supports extraction of both phrasal paraphrases and syntactically constrained paraphrases.


The other problem is that the context is not considered during phrasal paraphrase substitution, which causes a low paraphrasing accuracy.

the sizes of the augmented phrase tables are increased by 56% and 171% for the 29k set, 25% and 132% for the full set, which prove that our sentence novelty model has made considerable contributions to the enrichment of phrase tables.


The language resources were randomly split into three subsets for the evaluation of translation quality (eval, 1000 sentences), the tuning of the SMT model weights (dev, 1000 sentences) and the training of the statistical models (train).

all data sets were case-sensitive with punctuation marks preserved.


In general, exact optimization of ILP is NP-Hard.

the SMt system is bootstrapped using the seed training corpus S. the held-out set D is decoded by the SMt to obtain 1-best translation hypotheses.


If k sentences must be selected in a given active learning iteration, we use the greedy algorithm to prune the problem by choosing k > k sentences from the corpus, and subsequently construct the ILP on this smaller problem to select the required k sentences.

it is the knowledge of which source words were translated incorrectly that is useful for sample selection.


Indices of the following word chunks C k (j + 1 ≤ k ≤ |C|) are incremented by 1, and the separated word cew j becomes new C j+1 .

as for Semi-Markov, L is 10 and K is 191 in this experiment.


The numbers after L= indicate the maximum length of the word-chunks for Semi-Markov perceptron.

precision is defined to be the number of correctly recognized NEs divided by the number of all recognized NEs.


In addition, our method can use features extracted from word chunks that cannot be obtained in word-based NE recognitions.

in addition, our algorithm requires a lower maximum memory size than Linear-Chain and Semi-Markov.


Our proposed method showed good performance in this experiment, however, there is a drawback due to our recognizing strategy.

we limit the maximum length of the word-chunks.


Then we split the converted training data into five portions.

the computational cost will be higher than ever, when we recognize a large types of classes like Sekine's extended NE hierarchy that includes about 200 types of NEs for covering several types of needs of IE, QA, and IR (Sekine et al., 2002).


For example, in Figure 1, an EL system must return NIL for "C9 hasName(i, n) hasFirstWord(i, w), hasLastWord(i, w), isBlacklisted(w): the word sequence w is blacklisted, containsMoreSpecificMentions(i): the i-th gene mention collocates with more specific gene mentions in the current context.


Formula L.2 Formula L.2 is a hard constraint that must always hold whereas the others are soft and can be violated.

such an evaluation is more relevant to IE tasks such as the bio-molecular event extraction.


This shows that the saliency property is effective in instance-based evaluation.

we employed the greedy backward sequential selection algorithm (Aha and Bankert 1995) to select the optimized feature sets for ME NF with ten-fold cross validation on the training dataset.


Among them, 112 NEs refer to the two focused NEs, and less than 10 NEs in the comments refer to the other three NEs in the news article.

using no entities (K=0) and using all entities (K=100) will much lower the overall performance, which demonstrates that it is important to leverage appropriate named entities for feature generation in our proposed method.


The classification results are then used to cluster words into cognate sets.

in Section 4, we describe our method of clustering cognates.


We thus find the SUPERVISED LPF method not only impractical, but also rather unreliable.

our solution is to introduce a set of binary language pair features, one for each pair of languages in the data.


• CB2: selects the most frequently observed synset in the training data among the outputs of B1-B3.

finally, the synset which has the largest SVM score is selected as appropriate synset for trg.


The Japanese WordNet does contain other meanings for the term anime, such as a hard copal derived from an African tree (synset 14896018-n) and any of various resins or oleoresins (synset 14766265-n).

experiments show that the proposed method can identify synsets for 2,039,417 inputs at precision rate of 84%.


For Malt, we obtain an absolute LAS increase of 8.8% on the discussion forum data and an improvement of 5.6% on the Twitter data.

some progress has also been achieved in adapting parsers to new domains using semi-supervised and unsupervised approaches involving some labelled source domain training data, little, if any, labelled target domain data and large quantities of unlabelled target domain data.


What is needed is a sentence-splitter tuned to the punctuation conventions of Twitter.

one approach to the parser domain adaptation problem is to train a new system using large quantities of automatically parsed target domain text.


Unless otherwise stated, comparisons were significant with p < 0.05.

to our knowledge, our experiments are the largest in supervised MWE-token classification to date.


On typespecialised classification, our new idiom and WSD features achieve more consistent gains.

feature count cutoff of one was used to filter out uninformative features.


Some MWE-types have only an idiomatic meaning, such as the English greeting How do you do?, interpretation of which can be perplexing if attempted literally.

each instance in the corpus was preprocessed by running it through KNP to extract specific linguistic information.


have exclusively idiomatic meaning, other MWEtypes like the phrase kick the bucket may be idiomatic or literal depending on context.

the data show a definite positive trend with the number of instances, reaching 0.884 under a cap of 650 instances (and 589 average actual instances) per MWE-type.


In first order logic, if there is at least one predicate that violates formulas in a possible world 4 , the world is not valid.

figure 2: Example of a question sential question is the latter sentence.


In this model, we used the observed predicates for our model as features and trained a binary classifier for the questionanswer relation and a one-versus-rest classifier for the answer-answer relation.

we incorporate identification of these superrelations into our model.


In detail, we first extract pairs of queries and user-clicked questions from query logs, with which we induce question generation templates.

first, search engine query logs are powerful data for the research of query-toquestion generation, from which we have acquired a large volume of question generation templates.


The rest errors are questions that are well formed but less likely to be asked by people.

for example, query "故 宫 门 票 (The Imperial Palace / ticket)" can instantiate  where c(Qr, T p i ) is the frequency that Qr instantiates T p i in the query logs, c(Qr) is the frequency that Qr occurs in the query logs.


The extended class sequential rule model is called ExCSR hereinafter.

if there are one more than such rules, then choose the one with the least Distance-Constraint value; Step 4: R' = R'  {r}; R = R -{r}; Step 5: D = D-{instances satisfied by r}; Step 6: if D is empty, then return R'; Step 7: For each rR, update support(r) with regards to the updated D; Step 8: go to step 2.


For this purpose, we further to define a measure as Equation (2): Where label_value(i) denotes the value of the i-th distance label (the value of [NEIGH], [NEAR], [ANY] is set to 2, 1, 0.5 separately), and k is the total number of distance labels used in rule r. Obviously, shorter rules with simpler distance constraint are more general and preferred.

step 1: step 2:  = Ds-sPM(<>, 0, s); step 3: for each  in  (a) count the frequencies of all covered classes (the classes that the covered sequences belong to), and find the most frequent class label yY; (b) if support(y)  min_sup and confidence(y)  min_conf then output y.


Example rules with same support and confidence Rule 1 and 2 are with the same confidence 100% and support 32 thus have the same interestingness.

most of the work follows the automatic methods, which will be also adopted in our work.


(2009) ranked questions with several CQA specific features.

among the 12, 880, 882 questions we have collected, only 1.87% occur more than once.


This was repeated for 4 rounds (400 words), which took approximately 1.5 hours total.

overall, we believe that the existing online communication framework proved quite effective in rallying and organizing members for the project.


Overall, we believe that the existing online communication framework proved quite effective in rallying and organizing members for the project.

in addition, this allowed us to clarify the framework required, and develop tools that can be used to allow for provision of information on an even larger scale in future disaster situations.


We chose to verify and match the information by hand to prevent the provision of misinformation on a sensitive topic such as safety of earthquake victims.

the largest challenge in the project organization was the initial underestimation of the outpouring of support that the project would see.


This method requires labeled training data which is often difficult and expensive to produce.

overall, the product data cleansing solution is achieved by a collection of rule sets, each tackling a given product vertical.


The output of these rules are used to populate Data Warehouses and Product Information Management (PIM) systems.

finally, a domain expert writes rules to move entries into appropriate database columns and complete the standardization.


Section 4 provides details of the proposed generative model for extracting bilingual topic hierarchies from an unaligned bilingual corpus.

the internal nodes formed during the hierarchical clustering process shares words with their children.


Based on the common structure of Q&A forum threads, we will use the following definition to capture near-duplicate threads: Definition 1 Near-Duplicate Thread -Two threads are near-duplicate with each other in Q&A forum: (1) if both of their question and answer parts are the same with each other, or (2) if their question parts are same and one of the answers contains additional information compare to another answer.


in the entity linking, if the selected entity in the Wikipedia KB is not included in the TKB, our system will return NiL.


This system includes 2 components: candidate selection and disambiguation, in which the disambiguation part is based on the graphical method we described in section 3.2.

the NIL accuracies decrease and the overall accuracies have no obvious changes.


Then, for example, the probabilities of words given t can be obtained using Eq.(5).

this is especially true if there are few relevant documents.


We implemented pseudo RF using our method for n = 10, and compared the results with the three (re-)ranking results described above.

for each document in the search results, we construct a hybrid language model, which we call the HYB-based language model.


, β K ) represents the distributions over words for each topic z k .

estimation with the Newton-Raphson method has the disadvantages that it takes a long time and each estimated α k can be a negative value under certain circumstances.


Of the total 67 person entities in our test data, the IE-pipeline is not able to extract any employment information for 12 of them.

freebase 1 is a freely available online database of structured knowledge.


Generally speaking, the articles in news domain usually contain descriptions of date and events accompanying the publication sequences.

acknowledgments The authors would like to thank the anonymous reviewers for their comments on this paper.


Meanwhile, there are more than two summarization sentences or their similar ones may co-occur in a source document.

if there is no edge from ss k to ss i in G, then add an edge to G; else if weight(ss i , ss k ) is greater than the weight of existing edge, then update the edge.


Given a standard ordering, randomly produced orderings of the same objects would get an average τ of 0.

we believe the source documents can give us some effective and efficient information.


How to conduct an efficient and effective method for sentences ordering is a difficult but important task for both multidocument summarization and other text processing job, e.g.

chronological information cannot be easily extracted from those non-news documents and constructing a large corpus also is not so easy.


An abstract summary on the other hand is a summary where the text has been broken down and rebuilt as a complete rewrite to convey a general idea of the original text.

it should be noted that no preprocessing in terms of stop word removal and stemming were performed during the ROUGE evaluation since the package is tuned for English and no Swedish lexicon for that purpose were available at the time.


This work was partially funded by the LiveMemories project (www.livememories.org).

arg2 Labels  The sense of the connective (F2) refers to one of the four top-level classes in PDTB sense hierarchy, namely TEMPORaL, COMPaRISON, CON-TINGENCY and EXPaNSION.


To obtain this baseline, we take into account that i) Arg2 is the argument immediately adjacent to the connective and ii) 90% of the relations in PDTB are ei-ther intra-sentential or involve two contiguous sentences.

as for the relations considered, we focus here exclusively on explicit connectives and the identification of their arguments, including the exact spans.


Our approach is motivated by two intuitions: first, the identification of Arg2 and Arg1 may require different features, since the two arguments have different syntactic and discourse properties, as discussed in Section 3.

we increase the number of features at each step, and report the corresponding performance.


Relational lasso controls such relations underlying features by introducing a penalty parameter for each feature.

the procedure obtains three kinds of learning data input, a relation among features, and parameter values.


On the other hand, our α is estimated given a noisy relation among features, thus it plays a different role from w. In this sense, the way to handle this additional parameter for relational lasso is completely different from adaptive lasso.

this shows that the use of underlying information among features enhances the overall performance.


The relations between features are denoted as R, which is provided to the proposed algorithm and used to estimate α. R denotes a pairwise dependence relation between features.

the target function has two terms, one for fitting and another for regularization.


On the aspect of classification accuracy, MetaTD catches up with the 1-vs-Rest approach.

the experiments are run on four 64-bit computers with multi-core 1.9GHz AMD CPUs.


The slot nodes are used in such a way that the adopted TK function can generate fragments containing one or more children.

include predictions and expectations reported in the press.


While the removal of isArg drops the precision and saves the recall, the removal of delete is the other way around.

first-order logical formulae such as formulae (2) and (3) become the feature templates of MLN.


Because our approach deals with the all three cases by one joint model and ga-case is dominant in the data, it extracts more numbers of ga-case than the others.

they created three separated models corresponding to each of the case; ga (Nominative), wo (Accusative), and ni (Dative).


In practice, we also need to choose a training regime (in order to learn the weights of the formulae we added to the MLN) and a search/inference method that picks the most likely set of ground atoms (PA relations in our case) given our trained MLN and a set of observations.

our method can extract such important information better than previous work.


So, their models neglect the dependencies between cases.

extraction of inter-sentential PA relations which are crossing sentence boundaries is intractable.


Once again, the strongest feature was one of the LSA set, feature 12 (LSA similarity score of the target sentence with all the sentences of the abstract classified as Purpose).

we have measured the agreement between two human annotators by the Kappa statistics over a randomly selected subset of 46 sentences of the corpus and was around 0.7 (see Table 8).


A total of 2,293 sentences were automatically annotated and manually revised.

the noise from the automatic annotation of rhetorical structure does not interfere in the coherence annotation.


In this model, the generative process involves: (1) three subjectivity labels for sentences (i.e., sentence expresses subjective opinions as being positive/negative, or states facts as being objective); (2) a sentiment label for each word in the sentence (either positive, negative, or neutral), and (3) the words in the sentences.

the formal definition of the subjLDA generative process is as follows: In practice, it is quite intuitive that one classifies a sentence as subjective if it contains one or more strongly subjective clues (Riloff and Wiebe, 2003).


We explored incorporating two subjectivity lexicons as prior knowledge for subjLDA model learning, namely, the subjClue 3 and SentiWordNet 4 lexicons.

to most of the existing approaches requiring labelled corpora or linguistic pattern extraction, we view this problem as weakly-supervised generative model learning where the only input to the model is a small amount of domain independent subjective/neutral words.


Hu and Liu (2004) proposed a technique based on association rule mining to extract frequent nouns and noun phrases as product aspects.

given an application domain corpus, the user first provides a few seed resource terms.


A resource in a domain is often an aspect or implies an aspect.

there are some important differences between resources and other types of aspects.


Op, the performance figures remain higher than with the non-contextual Op classifier.

(1.b), (2.b) and (3.c) convey implicit opinions and (4.a) is subjective, but nonevaluative.


In the second step, the EDUs which contain at least one token that belongs to our subjective lexicon were retained for a further segmentation.

using discourse for SA raises new issues: Is sentence/clause subjectivity-based analysis appropriate?


Moving to the clause level is also not appropriate, since several opinion expressions can be discursively related as in The movie is great but too long where we have a Contrast relation or as in Mr. Dupont, a rich business man, has been savagely killed where we have an Elaboration because the appositive gives further information about the eventuality introduced in the main clause.

for both setups, we have used the SVM-light software package 3 .


On the Test data, contextual features detect more subjective (explicit or implicit) EDUs than without them.

the study of this hypothesis falls out of the scope of this paper and is therefore left for future work.


In addition, since we have already built an unsupervised morph analyzer as described in section 3.1, which is acquired by the same bootstrapping algorithm, we would like to make use of them as well, in a straightforward way: as long as a word can be analyzed by the acquired morph analyzer, it should not be output as closed-class items.

we experiment with four lists of closed-class words of the top 200 words acquired by the 'baseline-typeC', 'baseline-tokenC', 'bootstrap-typeC' and 'bootstrap-tokenC' respectively, as introduced in section 3.2.


Finally, to demonstrate the value of such a highly pure list of closed-class words, which previously could not be acquired through unsupervised learning, we plug the acquired list into a previously existing minimally supervised tagging system, (Zhao  Marcus, 2009), which only requires a closedclass lexicon for tagging all words.

after running the bootstrapping algorithm for words, we have already obtained a list of words that can be used for filtering the output of the baseline models.


This requests that the pivot transliteration unit must be consistent in the two individual modes.

table 5 reports the performance using the default setting of eq.


For words that do not exist in the dictionary, the decoder still considers every possible tag.

at each step, a REDUCE-LEFT/RIGHT action a adds a set of delayed features to the delayed feature vectors of state ψ: where Φ 1 (ψ, a) and Φ 2 (ψ, a) are the first-/secondorder delayed features generated by action a being applied to ψ.


Note that the above formulation with the delayed features is equivalent to the model with full look-ahead features if the exact decoding is performed.

we propose to use the features shown in Table 3 (b).


Within the other structures, we distinguish three types of features: deletion, permutation and transformation.

knowing what type of interrogative adverb corresponds to an adverb allows it to be categorized semantically), and paraphrasing features (e.g.


not valid) for the lexical entry corresponding to the row.

the tables have been converted into an interchange format, based on the same linguistic concepts as those handled in the tables.


For example, the English Penn Treebank does not represent the syntactic structure of prenominal nominal and adjectival modifiers, even though it is generally assumed that such structure exists.

for each linguistic phenomenon Φ, we ask two questions: (1) is Φ captured in both DS and PS guidelines?


• a second table of conversion functions is consulted for each word in the dependency tree after the fundamental algorithm has been applied.

further development of the Thai CDG formalism is also expected, in particular for the analysis of sentence-like noun phrases.


(Eisner, 1996) proposed inserting an embedded NP into each PP to solve this problem.

the dependency labels obtainable with absolute certainty in this way are often of the less interesting kind -e.g.


In  particular the data have been collected not only from French radio channels, but also from a North-African French-speaking radio channel.

the higher accuracy reached with this type of features is paid at training time with the number of feature function generated in situations like the one reported above, and makes direct CRF model training unfeasible.


0 and GEN signify null postposition and genitive postpositions respectively By distinguishing intra-clausal structures from inter-clausal structures, the 2-Hard setup is using shallower trees and is able to take better global decisions by using more contextual information.

this task comprises of two sub-tasks, a) relative clause identification from the 1 st stage output and b) identifying the head of the relative clause from the matrix clause.


One such case was the absence of morphological features of lexical items in the 2 nd stage for 2-Hard.

the POS category and the lexical item of the elements in Stack and Input buffer are more crucial for 2 nd stage relations than for the 1 st stage specific relations.


The total number of inversion pairs of σ, also called inversion pair cardinality of σ, is a proper measurement of the distance between σ and the identity permutation (1, 2, .

our goal is to recover the correct, or reasonable, word order of the target sentence.


The difference is that the source sentence should be a tree instead of a string and additional syntactic constraints operate.

we can not simply calculate the outside probability of the hierarchical rule using the product of outside probabilities of phrase pair and subphrase pairs.


The fluency can be better addressed by incorporating morphological information such as proper handling of the case markers of the MNEs.

this significant presence of these entities plays an important role in the news corpus.


The RMWEs in Manipuri are classified as: (i) Complete RMWEs, (ii) Partial RMWEs, (iii) Echo RMWEs and (iv) Mimic RMWEs.

the presence of non-NE transliterated entities is lesser and estimated at 9.2%.


we detail the scheme on the Bi-word models, the Mono-word models can be handled analogously.


This is different for the conditional models, which are easier to handle but where most approaches are based on initializing from single-word based models (Brown et al., 1993;Vogel et al., 1996;Al-Onaizan et al., 1999).

our comparison of projected gradient descent (PGD) and expectation maximization (EM) revealed that EM leads to better alignments, although PGD finds a comparable but slightly higher objective value.


In this paper we much generalize on this work, considering a class of models we term Bi-word models.

the initial probability and the inter-alignment model share the parameters p 0 and p 1 .


Given the observation that more recent training data seems to be more important than older one, we apply an exponential decay function: where λ is the decay factor and ∆t is the discretized time distance (0 for most recent part, 1 for the next one, etc.).

of course, we expect to achieve better results by finding the optimal weighting between recent and ancient data.


We extend the traditional HMM to allow a broader range of phone mapping configurations.

we propose to estimate this probability by a combination function of similarity function and translation table.


To test CLIR with multiple transliterations, we need a document collection with controlled multiple transliterations.

the null transition (Bahl et al., 1982) is used to represent skipping a state without consuming any observations.


2003) on a development set of 500 sentences.

during testing, as the reference translations cannot be used, test sentences are converted into a lattice (Dyer et al., 2008) where two alternate paths are included, one with the redundant source word removed and another with the redundant source word as is.


The following is an illustration of the kind of improvements clause-based translation brings: Input: America claims that Iran wants to continue its nuclear programme, and secretly builds atomic weapons.

simply translating non-finite clauses separately with reordering constraints around them, will not lead to good translation, because the translation of these clauses is often dependent on the superordinate clause, and also there is reordering between these clauses and the superordinate clause.


Apart from the BTEC 3 corpus available through the IWSLT 4 competition and Holy Bible datasets described in (Paul, 2008) and , respectively, there is a recent release of a six language parallel corpus (including both Chinese and Spanish) from United Nations (UN) for research purposes (Rafalovith and Dale, 2009).

this popular system implements a log-linear model in which a source language sentence f J = f 1 , f 2 , .


We experiment with the parallel Chinese-Spanish corpus (United Nations) to explore alternatives of SMT strategies which consist on using a pivot language.

in this study we use the UN corpus taking advantage of the fact that (as far as we are concerned) it is the biggest parallel corpus freely-available in Chinese-Spanish and it contains the same sentences in six other languages, therefore we can experiment with different pivot languages.


The near-synonyms were then removed from the test examples for FITB evaluation.

near-synonyms are not necessarily interchangeable in contexts due to their specific usage and syntactic constraints.


This paper addresses the issue of cluster labeling and presents a method for assigning labels by using concepts in a machinereadable dictionary.

the data we used is RWCP corpus labeled with UDC codes selected from 1994 Mainichi newspaper (RWC, 1998).


(4), hy(x) refers to the hypernym of a word x. min(dis(hy(w i ), w i )) shows the minimum distance between hy(w i ) and w i .

the suggested terms, even when related to each other, tend to represent different aspects of the topic underlying the cluster, and it is often the case that a good label does not occur directly in the document.


And the probability of a token that has occurred c times in that context before is c − 1 2 n This allocation strategy is called PPMD (Howard, 1993) and has shown great performance in text compression.

extended to adopt more elaborated patterns such as Kozareva et al.


Each prediction takes the form of a probability distribution that is provided to an encoder, which is usually an arithmetic coder.

such a task aims to extract instances belonging to a specific category such as acquiring Tom Hanks and Al Pacino into a list containing other actors.


TinySVM version 0.09 1 was used for SVM training and classification.

it is not clear how we can effectively use the probabilities of different cases c; ρ i (c) for some cases would be reliable, while some others less reliable.


The two vector-based WSI approaches use k-means algorithm to cluster the context vectors of target words and the maximum number of k-means iterations is set to 100.

for each cluster C j , we generate instance pairs, in which is the total number of instances that belong to C j .


Our findings indicate that the lexicon-based method is highly competitive with the supervised, task-specific method.

an even more individualized vector classification model, in the form of individual k values for each pair, does not improve performance.


We also removed pairs where one of the words was quite rare (fewer than 150 tokens in the entire corpus) or where, based on examples pulled from the corpus, there was a common confounding homonym-for instance the word prob, which is a common clipped form of both problem and probably.

the agreement is surprisingly low; even if "I'm not sure" responses are discounted, agreement with the writer's choice gold standard is just 71.7% for the remaining datapoints.


But neither ESA nor WNP recognize collocations: the former because of the bag-of-words principle underlying tfidf, and the latter only in the case where the collocational pair is a concept on its own.

we found that this measure provides bad results in its lower range (since the path length between distant nodes strongly depends on the density of wordNet for each knowledge domain).


Most of the product attribute extraction techniques in literature work on structured descriptions using several text analysis tools.

paid WAAAAY to much for it at the time th0.. it sellz now fer like a third the price I paid.. heheh.. oh well....the fact that I didn't wait a year er so to buy a bigger model for half the price.. most likely from a different store.. ..not namin any namez th0.. *cough*BBHOSEDMe*cough* The italicized terms are some product attributes discussed in this review.


We call such words as Wikipedia words and if cannot be mapped, we refer them as Non-Wikipedia words in later sections of this paper.

for a Wikipedia words set {x1, x2, x3, ....x k }, semantic relatedness of xi to the context is given by The applicability of CR feature is justified in terms of high scalability and the ever growing knowledge of Wikipedia.


"d‚k:$| price is a little low" obtains 1080 hits while "d‚k:p| price is a little high" obtains 19400 hits.

we will just focus on CPs in this paper and leave WPs for future work.


This kind of answer can be extracted by using a method based on machine learning techniques (Isogai et al., 2009).

because of this large numbers of questions, questioners had better submit questions which give enough information to answerers.


In other 90 cases, questioners resubmitted TYPE (Q2-1), (Q2-2), and (Q2-3) questions, in other words, they knew or could find out easily what answerers indicated.

for example, in (Q 6), the questioner described the solution received from the answerer of (A 5) was unhelpful to solve his/her problem.


Then, there were 17 of these 107 cases where questioners resubmitted TYPE (Q2-4) questions, in other words, they had unknown points in indications received from answerers and needed to ask about what they were.

there were 107 cases where questioners resubmitted TYPE (Q2-1), (Q2-2), (Q2-3), and (Q2-4) questions, in other words, they accepted indications from answerers and modified their questions.


The reason is that since the predicted types of a question are already diversified by CogQTaxo, incorporating it into question re-ranking already enables us to diversify the infoNeeds in the results implicitly.

it reflects the real user infoNeeds distribution.


In this data set, an argument is not always a sentence.

in this data set, an argument is not always a sentence.


In this paper, we show that the accuracy of a discriminative sequential POS tagger can be substantially improved by exploring syntactic features.

this model can be regarded as the integrated model of both Perceptron-based and CRF-based models.


Given an input sentence x = w 1 ...w n , we denote its POS tag sequence by t = t 1 ...t n , where t i ∈ T , 1 ≤ i ≤ n, and T is the POS tag set.

pOS tagging is traditionally considered as a supporting task for dependency parsing.


Recently, extensive research on Chinese POS tagging has been done.

the introduction of long-distance dependencies can largely reduce this difficulty.


As the confidence values are low, major arcs in these sentences might be wrong.

we consider two types of raw corpus, one from same domain as of training and testing data and the other from different domain.


All the above mentioned works are on phrase structure parsing of English.

these sentences were giving negative impact on the parser performance.


The usefulness of the corpus may be gauged in two ways.

this corpus is intended to lay the foundation for this direction of research.


For any contrastive studies between Cantonese and Mandarin, a corpus balanced between formal and colloquial registers would be desirable.

the three differences for which no examples exist are the following.


For example, Cantonese is spoken by more than 52 million people, mostly in southern China and overseas Chinese communities.

for some expressions of kinship, the marker is not simply omitted but replaced by a classifier, such as 我個仔 ngo-go-zai 'I CL son' 'my son'.


The µ parameter was tested on the [100,1200] range for Res-PubliQA and Yahoo!

they use the same WordNet-based relatedness method in order to expand documents, following the BM25 probabilistic method for IR, obtaining some improvements, specially when parameters had not been optimized.


All senses of the ambiguous word are provided as optional answers.

here, the two sentences are "根据这 一 方 案 (according to the scheme)" and "这 种 状况带来了两个弊端 (this situation brings two drawbacks)" respectively.


Although it is important to use powerful machine learning algorithms, latest studies have found that large-scale and high-quality corpora are more important for WSD (Agirre and Edmonds, 2006).

the two users are asked to input as many synonyms of the same ambiguous word in the sentence as possible within a limited pe-riod of game round time.


In relation extraction, syntactic relatedness between the candidate entities of a relation is usually considered an important cue (Zhou et al., 2005;Mintz et al., 2009).

we also investigate whether healthiness correlates with sentiment.


Note that this data set does not have any annotations.

we say that q 1 and q 2 are inconsistent in segmentation if there exist more than one common subsequence of tokens having different segment boundaries.


for query q is then defined as a sequence of nonoverlapping segments.

it is worth studying other methods that can address such performance gap.


At each node, we select children to which edge classifiers return positive scores (Lines 4-7).

it seems safe to conclude that local training caused overfitting.


contextually bound, (EER: 1:100), see Example (6) from PCEDT.

the sentence members that appear only implicitly in the sentence (as the Addressee in this case) are not supposed to carry some new, important information (because their presence in the /surface part of the/ sentence is not necessary) and therefore they are automatically pre-annotated as contextually bound (furand the tectogrammatical layer) are displayed as small circles in the figure.


ko is approximated to dative while se is approximated to instrumental case.

the extraction of distributional counts is simple and straightforward in Hindi.


• Nominal Ambiguity: As a matter of fact, animacy is an inherent and a non-varying property of nominal referents.

the ambiguity in these case makers, however, has a profound impact on our results as discussed in Section 5.


A clustering experiment is performed with FCM clustering algorithm on SET-1 and SET-2, with parameters c 'number of clusters' and m 'degree of fuzziness' set to 2.

our clustering of these nominals is justified.


To extract mention properties, we have to compute the head.

to generate a training example we append "C-" to one NP node, keeping all the remaining nodes as-is.


If a spurious mention is introduced, the CR system might still assign it to no coreference chain and thus discard from the output partition.

any MD method for OntoNotes would rely on parsing.


Another shortcoming of previous classification approaches is that they only focus on detecting the overall topical category of a document.

table 2 presents the word statistics of the prior lexicons generated using these two different methods 6 .


We propose a novel approach of deriving word prior knowledge using the measurement of relative entropy of words (RWE).

the task of classifying tweets as violence-related poses different challenges including: high topical diversity; irregular and ill-formed words; event-dependent vocabulary characterising violence-related content; and an evolving jargon emerging from violent events.


(1)Linkbased models rely solely on hyperlinks, without considering content-based features.

the first observation is that all of the content features are discriminative.